# -*- coding: utf-8 -*-

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
# 读取数据集
mnist = input_data.read_data_sets("G:\\Learingspaces\\wesley\\mnist\\MNIST_data", one_hot=True)
# 每个批次大小, 一次放入100张图片
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 输入数据, None表示任意值, 784 表示 28*28 的图片转换为1维向量
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

keep_prob = tf.placeholder(tf.float32)
# 创建神经网络，两层隐含层
W1 = tf.Variable(tf.truncated_normal([784, 1000], stddev=0.1))
b1 = tf.Variable(tf.zeros([1000]) + 0.1)
L1 = tf.nn.tanh(tf.matmul(x, W1) + b1)
L1_drop = tf.nn.dropout(L1, keep_prob)

W2 = tf.Variable(tf.truncated_normal([1000, 1000], stddev=0.1))
b2 = tf.Variable(tf.zeros([1000]) + 0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2)
L2_drop = tf.nn.dropout(L2, keep_prob)
# 输出层
W4 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.1))
b4 = tf.Variable(tf.zeros([10]) + 0.1)
# softmax返回概率
prediction = tf.nn.softmax(tf.matmul(L2_drop, W4) + b4)

# 损失函数
# loss = tf.reduce_mean(tf.square(y - prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
# 随机梯度下降法
lr = tf.Variable(0.1, dtype=tf.float32)
train_step = tf.train.GradientDescentOptimizer(lr).minimize(loss)
# 初始化变量
init = tf.global_variables_initializer()
# 测模型准确率
# tf.equal   求等，相等true，不同false
# tf.argmax  返回一维tensor中的最大值位置
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))
# 准确率
# tf.cast    类型转换，将类型转换为float32
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# path = "./log"
with tf.Session() as sess:
    sess.run(init)
    # 迭代次数
    for epoch in range(10):
        sess.run(tf.assign(lr, 0.1 * (0.95 ** epoch)))
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0})

        test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
        train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0})
        learning_rate = sess.run(lr)
        print("Iter:" + str(epoch) + ",Testing Accuracy:" + str(test_acc) + ", Training Accuracy: " + str(train_acc) + ", Learning Rate: " + str(learning_rate))
        # tf.summary.FileWriter(path, sess.graph)
